PresentaciĆ³n genes antisentido
Juan Jose Rojo Carrillo
6/27/2022
Paquetes utilizados:
library(readr)
library(DT)
library(nortest)
library(ggplot2)
library(nortest)
library(dplyr)
library(RColorBrewer)
library(gridExtra)
library(readxl)1 Genes codificantes sin AS y con AS
1.1 Tabla de genes codificantes sin AS
datatable(coding_filtro)1.2 Tabla de genes codificantes con AS
datatable(tabla_final_genes)1.3 ComparaciĆ³n de tamaƱo.
barplot(c(median(tabla_final_genes$size),(median(coding_filtro$size))),names.arg = c("Si","No"), main = "Mediana tamaƱo", xlab="TamaƱo (pb)",legend.text = c("Si","No"), col=c("lightcoral","lightblue"),horiz = TRUE,las=1, cex.names = 0.9,cex.axis=0.8)
Resumen datos Genes con AS
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 595 22324 63394 157223 171915 2304996lillie.test(tabla_final_genes$size)##
## Lilliefors (Kolmogorov-Smirnov) normality test
##
## data: tabla_final_genes$size
## D = 0.26838, p-value < 2.2e-16Resumen datos Genes sin AS
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 38 9232 25956 61483 66092 2473538Test de normalidad:
lillie.test(coding_filtro$size)##
## Lilliefors (Kolmogorov-Smirnov) normality test
##
## data: coding_filtro$size
## D = 0.29572, p-value < 2.2e-16Test comparativo de distribuciones no paramƩtricas
wilcox.test(coding_filtro$size,tabla_final_genes$size)##
## Wilcoxon rank sum test with continuity correction
##
## data: coding_filtro$size and tabla_final_genes$size
## W = 9457736, p-value < 2.2e-16
## alternative hypothesis: true location shift is not equal to 01.4 ComparaciĆ³n de transcritos alternativos.
proporcion_no_AS <- table(total_gene_no_AS$V7) #Contabilizar el nĆŗmero de transcritos
transcript_no_AS<- rbind(as.data.frame(proporcion_no_AS))
summary(transcript_no_AS$Freq)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 3.000 6.000 8.362 11.000 1211.000lillie.test(transcript_no_AS$Freq)##
## Lilliefors (Kolmogorov-Smirnov) normality test
##
## data: transcript_no_AS$Freq
## D = 0.27933, p-value < 2.2e-16lillie.test(tabla_final_genes$number_trans)##
## Lilliefors (Kolmogorov-Smirnov) normality test
##
## data: tabla_final_genes$number_trans
## D = 0.19158, p-value < 2.2e-16wilcox.test(transcript_no_AS$Freq,tabla_final_genes$number_trans)##
## Wilcoxon rank sum test with continuity correction
##
## data: transcript_no_AS$Freq and tabla_final_genes$number_trans
## W = 10948440, p-value < 2.2e-16
## alternative hypothesis: true location shift is not equal to 01.5 DistribuciĆ³n por cromosoma.
proporciones <- table(tabla_final_genes$chr)
proporciones_data_frame<- rbind(as.data.frame(proporciones))
proporciones_data_frame$Var1 <- factor(proporciones_data_frame$Var1,levels = c(seq(1:22),"X","Y"))#Proporciones para los genes que contienen AS
summa <- sum(proporciones_data_frame$Freq) #Suma del total de genes que aparecen
nuevo_data_frame <- proporciones_data_frame
for (i in 1:24){
valor_chr <- proporciones_data_frame[i,2]
normalizado <- valor_chr/summa #CĆ”lculo del nĆŗmero relativo
variable_chr <- proporciones_data_frame[i,1]
nuevo_data_frame[i,1] <- variable_chr
nuevo_data_frame[i,2] <- round(normalizado,4)
}
colnames(nuevo_data_frame) <- c("Chr_AS","Freq_ajustada")
datatable(nuevo_data_frame)#Proporciones para los genes que no tienen un AS
variable <- table(coding_filtro$chr)
df <- rbind(as.data.frame(variable))
summa1 <-sum(df$Freq)
for (i in 1:24){
valor_chr <- df[i,2]
normalizado <- valor_chr/summa1
variable_chr <- df[i,1]
df[i,1] <- variable_chr
df[i,2] <- round(normalizado,4)
}
datatable(df)par(mfrow=c(1,2))
variable_genes_con_AS <- table(tabla_final_genes$chr)
barplot_chr_AS <- barplot(variable_genes_con_AS[c(seq(1:22),"X","Y")],font.axis=2,las=2,col=c("lightcoral"),ylab="Frecuencia", main = "NĆŗmero de genes",legend.text = "AS")
variable_genes_sin_AS <- table(coding_filtro$chr)
barplot_chr_genes <- barplot(variable_genes_sin_AS[c(seq(1:22),"X","Y")],font.axis=2,las=2, main="por cromosoma",col=c("lightblue"),ylab="Frecuencia", legend.text = "Sin AS")
1.6 Secuencias Repetidas
1.6.1 Referencia
total_repeat <- table(total_Repeat$family_repeat) #Calculo del tipo de repeticiĆ³n mĆ”s repetida
barplot(total_repeat,font.axis=2,las=2, main="Frecuencia ApariciĆ³n Secuencias Repetidas",col=c("lightcoral"),ylab="Frecuencia",cex.names = 0.65,cex.axis=0.6)
total_repeat <- table(total_Repeat$type_repeat) #Contabilizar las veces que se repite cada repeticiĆ³n y la familia a la que pertenece
barplot(total_repeat,font.axis=2,las=2, main="Frecuencia ApariciĆ³n Secuencias Repetidas",col=c("lightcoral"),ylab="Frecuencia",cex.names = 0.65,cex.axis=0.6)
total_repeat_table<- rbind(as.data.frame(total_repeat))
#datatable(total_repeat_table,colnames =c("Nombre Gen","Frecuencia"))suma_repe <- sum(total_repeat_table$Freq)
mat = matrix(ncol = 0, nrow = 0)
df=data.frame(mat)
for (i in 1:13){
valor_chr <- total_repeat_table[i,2]
normalizado <- valor_chr/suma_repe
variable_chr <- total_repeat_table[i,1]
df[i,1] <- variable_chr
df[i,2] <- round(normalizado,7)
}
datatable(df)1.6.2 Codificantes con AS
gene_prot_coding_repeat <- table(intersect_gene_repeat$type_repeat)
barplot(gene_prot_coding_repeat,font.axis=2,las=2, main="Frecuencia ApariciĆ³n Secuencias Repetidas En Genes Codificantes con AS",col=c("lightcoral"),ylab="Frecuencia",cex.names = 0.65,cex.axis=0.6,cex.main=1)
repeat_gene_prot_coding<- rbind(as.data.frame(gene_prot_coding_repeat ))
#datatable(repeat_gene_prot_coding)suma_repe <- sum(repeat_gene_prot_coding$Freq)
mat = matrix(ncol = 0, nrow = 0)
df=data.frame(mat)
for (i in 1:13){
valor_chr <- repeat_gene_prot_coding[i,2]
normalizado <- valor_chr/suma_repe
variable_chr <- repeat_gene_prot_coding[i,1]
df[i,1] <- variable_chr
df[i,2] <- round(normalizado,7)
}
datatable(df)repeat_gene <- table(intersect_gene_repeat$name_gen) #CƔlculo del numero de veces que aparece cada gen
repeat_gene_data_frame<- rbind(as.data.frame(repeat_gene))
datatable(repeat_gene_data_frame)1.7 Codificantes sin AS
prot_coding_repeat <- table(intersect_total_gene$family)
barplot(prot_coding_repeat,font.axis=2,las=2, main="Frecuencia ApariciĆ³n Secuencias Repetidas En Genes Codificantes sin AS",col=c("lightblue"),ylab="Frecuencia",cex.names =0.65,cex.axis=0.6,cex.main=1)
coding_total <- table(intersect_total_gene$type_repeat)
barplot(coding_total,font.axis=2,las=2, main="Frecuencia ApariciĆ³n Secuencias Repetidas En Genes Codificantes sin AS",col=c("lightblue"),ylab="Frecuencia",cex.names =0.65,cex.axis=0.6,cex.main=1)
repeat_gene_data_frame_1<- rbind(as.data.frame(coding_total))
#datatable(repeat_gene_data_frame_1)suma_repe <- sum(repeat_gene_data_frame_1$Freq)
mat = matrix(ncol = 0, nrow = 0)
df=data.frame(mat)
for (i in 1:13){
valor_chr <- repeat_gene_data_frame_1[i,2]
normalizado <- valor_chr/suma_repe
variable_chr <- repeat_gene_data_frame_1[i,1]
df[i,1] <- variable_chr
df[i,2] <- round(normalizado,7)
}
datatable(df)2 Gen antisentido y Gen Sentido.
2.1 Tabla de genes AS
datatable(tabla_final_AS)2.2 Tabla conjunta.
nuevo_out <- dplyr::left_join(primer_filtrado,prot_coding_comparar_1556,by=c("X8"= "X6")) #ReuniĆ³n de las tablas generadas con left join para comprobar que ningĆŗn gen queda sin su pareja
nuevo_out$X1.y <- gsub('chr', '',nuevo_out$X1.y) #Con gsub se elimina la parte de "chr" antes del nĆŗmero del cromosoma para mantener el mismo formato
nuevo_out <- nuevo_out[,c(1,2,3,4,5,7,8,9,10,11,12,13)]
datatable(nuevo_out,colnames = c("Id_ens_AS","chr_AS","Pos_init","Pos_fin","Strand","AS","Gene","Chr_gene","Pos_init_gen","Pos_fin_gen","Strand_gene","ID_gene"))2.3 ComparaciĆ³n TamaƱo.
barplot(c(median(tabla_final_AS$size),(median(tabla_final_genes$size))),names.arg = c("AS","S"), xlab="TamaƱo (pb)",main = "Mediana tamaƱo", col=c("lightcoral","lightblue"),horiz = TRUE,las=1, cex.names = 0.9,cex.axis=0.8)
#legend("bottomright", legend = c("Antisentido","Sentido"),fill=c("lightcoral","lightblue"))tabla_conjunta$type <- factor(tabla_conjunta$type,levels=c("GEN","AS"), labels = c("S","AS")) #ModificaciĆ³n del nombre referido a cada tipo de genp <- ggplot(tabla_conjunta, aes(x=type, y=size,fill=type)) +
geom_boxplot(outlier.size=1,outlier.shape=5)+ scale_y_continuous(name = "TamaƱo (pb)")+scale_x_discrete(name =" " )+theme_classic()+theme(plot.title=element_text(hjust=0.5))+coord_flip()+labs(title="DistribuciĆ³n de TamaƱos")+ theme(legend.position="none")
p
Resumen datos AS
summary(tabla_final_AS$size)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 330 3410 10380 38184 34290 1064401#Test de normalidad para el tamaƱo de los genes AS
lillie.test(tabla_final_AS$size)##
## Lilliefors (Kolmogorov-Smirnov) normality test
##
## data: tabla_final_AS$size
## D = 0.32095, p-value < 2.2e-16Resumen datos gen
summary(tabla_final_genes$size)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 595 22324 63394 157223 171915 2304996#Test de normalidad para el tamaƱo de los genes AS
lillie.test(tabla_final_genes$size)##
## Lilliefors (Kolmogorov-Smirnov) normality test
##
## data: tabla_final_genes$size
## D = 0.26838, p-value < 2.2e-16#ComparaciĆ³n de medianas mediante un test no parĆ”metrico.
wilcoxon <- wilcox.test(tabla_final_AS$size,tabla_final_genes$size)
wilcoxon##
## Wilcoxon rank sum test with continuity correction
##
## data: tabla_final_AS$size and tabla_final_genes$size
## W = 573754, p-value < 2.2e-16
## alternative hypothesis: true location shift is not equal to 02.4 ComparaciĆ³n de transcritos alternativos
barplot(c(median(tabla_final_AS$number_trans),(median(tabla_final_genes$number_trans))),names.arg = c("AS","GEN"), main = "Mediana NĆŗmero TrĆ”nscritos", xlab="NĆŗmero de TrĆ”nscritos",col=c("lightcoral","lightblue"),horiz = TRUE,las=1, cex.names = 0.9,cex.axis=0.8)
Resumen Transcritos AS
summary(tabla_final_AS$number_trans)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 1.00 2.00 4.73 5.00 283.00Resumen Transcritos Genes con AS
summary(tabla_final_genes$number_trans)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 4.00 8.00 11.14 14.00 192.00lillie.test(tabla_final_AS$number_trans)##
## Lilliefors (Kolmogorov-Smirnov) normality test
##
## data: tabla_final_AS$number_trans
## D = 0.35695, p-value < 2.2e-16lillie.test(tabla_final_genes$number_trans)##
## Lilliefors (Kolmogorov-Smirnov) normality test
##
## data: tabla_final_genes$number_trans
## D = 0.19158, p-value < 2.2e-16wilcox.test(tabla_final_AS$number_trans,tabla_final_genes$number_trans)##
## Wilcoxon rank sum test with continuity correction
##
## data: tabla_final_AS$number_trans and tabla_final_genes$number_trans
## W = 515374, p-value < 2.2e-16
## alternative hypothesis: true location shift is not equal to 02.5 Intersect y Coverage
frecuencia <- table(round(coverage_AS_gen_encabezado$value3,1)) #Se reducen los decimales que se contabilizan los datos para poder establecer relaciones
frecuencia_tabla <- rbind(as.data.frame(frecuencia))
datatable(frecuencia_tabla)barplot(frecuencia,main = "Solapamiento AS - GEN", xlab="Frecuencia",xlim=c(0,800),ylab="Grado de solapamiento",col=c("lightcoral"),horiz = TRUE,las=1, cex.names = 0.9,cex.axis=0.8)
genes <- table(intersect_AS_gen_encabezado$gene_name) #Veces que se solapa un antisentido con un gen sentido
porcentaje_tabla <- rbind(as.data.frame(genes))
datatable(porcentaje_tabla)2.6 AS
repeat_AS <- table(intersect_AS_repeat$type_repeat)
barplot(repeat_AS,font.axis=2,las=2, main="Frecuencia ApariciĆ³n Secuencias Repetidas Solapadas con AS",col=c("lightcoral"),ylab="Frecuencia",cex.names = 0.9,cex.axis=0.8)
repeat_AS <- table(intersect_AS_repeat$type_repeat)
prop_repeat_AS <- rbind(as.data.frame(repeat_AS))
#datatable(prop_repeat_AS)suma_repe <- sum(prop_repeat_AS$Freq)
mat = matrix(ncol = 0, nrow = 0)
df=data.frame(mat)
for (i in 1:13){
valor_chr <- prop_repeat_AS[i,2]
normalizado <- valor_chr/suma_repe
variable_chr <- prop_repeat_AS[i,1]
df[i,1] <- variable_chr
df[i,2] <- round(normalizado,4)
}
datatable(df)repeat_AS <- table(intersect_AS_repeat$name_gen) #Veces que se solapa un antisentido con una secuencia repetida.
repeat_AS_data_frame<- rbind(as.data.frame(repeat_AS))
datatable(repeat_AS_data_frame)3 ExpresiĆ³n higados humanos.
OUT_Prot_coding$associated_gene <- gsub('\\..*$', '',OUT_Prot_coding$associated_gene) #EliminaciĆ³n de los nĆŗmeros que hay despuĆ©s de los puntos
OUT_AS$associated_gene <- gsub('\\..*$', '',OUT_AS$associated_gene)
table_out_AS <- table(OUT_AS$associated_gene)
df_AS_total <- rbind(as.data.frame(table_out_AS))ID_AS <- Nombre_AS_Gen[,c(1,7)]
t1 <- ID_AS %>% distinct(X7, .keep_all = TRUE) #Filtrado para que no se repitan isoformas
ID_del_AS_con_GEN <- Nombre_AS_Gen[,c(1,8)]
t_id_AS_gene <- ID_del_AS_con_GEN %>% distinct(X8, .keep_all = TRUE)id_nombre <-dplyr::inner_join(df_AS_total,t1,by=c("Var1" = "X1"))
genes_AS_e_id <- id_nombre[,c(1,3)]
isoforma_id_nombre <-dplyr::inner_join(ID_del_AS_con_GEN,genes_AS_e_id,by=c("X1" = "Var1")) #ReuniĆ³n de tablas que coincidan en el ID del gen antisentidoOUT_Prot_coding$associated_gene <- gsub('\\..*$', '',OUT_Prot_coding$associated_gene)
table_out_gene <- table(OUT_Prot_coding$associated_gene)
df_gene_total <- rbind(as.data.frame(table_out_gene))ID_gene <- prot_coding_comparar_1556[,c(5,6)] #SelecciĆ³n del id del gen sentido con su nombre
t2<- ID_gene %>% distinct(X5, .keep_all = TRUE) #Filtrar ante posibles repeticiones del id.id_nombre <-dplyr::inner_join(df_gene_total,t2,by=c("Var1" = "X5")) #IdentificaciĆ³n del gen sentido que se expresa
id_nombre_gen <- id_nombre[,c(1,3)]isoforma_y_gen <- OUT_AS[,c(1,7)]
AS_no_Sentido <- dplyr::left_join(isoforma_id_nombre,id_nombre_gen,by=c("X8"="X6")) #Con left join seleccionamos los genes AS que se expresan en el hĆgaod, cuando aparezca una celda vacĆa la tabla significa que no se expresa el gen sentido.
id_AS_name_no_expr_sent <- AS_no_Sentido[c(2,3,7,17,20,21,26,28,29,32,33,37,42,47,48,53,55,56,63,64,67,70,75,77,79,85,86,97,104,107,108,112,114,116,124,128,130,140,142,146,147),c(1,3,2)]
datatable(id_AS_name_no_expr_sent)AS_matriz <- exprMatrix_AS
AS_matriz$ids <- gsub('_.*$', '',AS_matriz$ids) #EliminaciĆ³n del gen asociado a cada isoforma del archivo de la matriz de expresiĆ³n
isoforma_id_nombre <-dplyr::inner_join(isoforma_y_gen,ID_AS,by=c("associated_gene" = "X1")) #Relacionar la isoforma creada anteriormente con el otro fichero que relaciona a la isoforma con el id del gen
id_gen_noAS_noexprx <- id_AS_name_no_expr_sent[,c(1,3)]
isoforma_id_nombre <-dplyr::inner_join(isoforma_id_nombre,id_gen_noAS_noexprx,by=c("associated_gene" = "X1"))
uniq_matriz_AS_no_gene_id <- isoforma_id_nombre %>% distinct(isoform, .keep_all = TRUE)
datatable(uniq_matriz_AS_no_gene_id) #Genes Antisentido que no expresan el gen sentido3.1 Genes AS
Matriz_AS <- exprMatrix_AS
colnames(Matriz_AS) <- c("ID","OM001","OM010","OM012","OM015","OM019","OM025","OM028","OM029","OM032","OM04","OM05","OM06","OM009","OM33","OM49")
heatmap_AS <- Matriz_AS
heatmap_matrix_AS <- as.matrix(heatmap_AS[,2:16]) #Matriz de expresiĆ³n de los transcritos de genes AS
rownames(heatmap_matrix_AS) <- exprMatrix_AS$ids
heatmap(heatmap_matrix_AS,Rowv=NA,Colv=NA,col= colorRampPalette(brewer.pal(5,"YlOrRd"))(4),cexRow=0.01,cexCol = 0.7)
boxplot(Matriz_AS[,2:16],cex.axis=0.7,las=2,col=1:7,ylab="TPM",horizontal = TRUE)
A continuaciĆ³n se genera un grĆ”fico de la distribuciĆ³n de la expresiĆ³n de los genes antisentido por paciente. Se realiza una cuenta de la expresiĆ³n sin decimales para agrupar por nĆŗmero entero y se represtan al final todos en un grĆ”fico.
OM001 <- table(round(Matriz_AS$OM001,0))
OM001_df <- rbind(as.data.frame(OM001))
OM001_plot <- ggplot(OM001_df,aes(x=Var1,y=Freq,fill=Freq))+geom_col()+geom_line(group=1)+theme_minimal()+theme(legend.position="none")+labs(x="OM001")+theme(axis.title.x = element_text(face="bold", vjust=-0.5, colour="orange", size=rel(1)),axis.title=element_text(size=10,face="bold"))+theme(axis.text.x = element_text(angle = 90, hjust = 1),axis.text = element_text(size=6))OM010 <- table(round(Matriz_AS$OM010,0))
OM010_df <- rbind(as.data.frame(OM010))
OM010_plot <- ggplot(OM010_df,aes(x=Var1,y=Freq,fill=Freq))+geom_col()+geom_line(group=1)+theme_minimal()+theme(legend.position="none")+labs(x="OM010")+theme(axis.title.x = element_text(face="bold", vjust=-0.5, colour="orange", size=rel(1)),axis.title=element_text(size=10,face="bold"))+theme(axis.text.x = element_text(angle = 90, hjust = 1),axis.text = element_text(size=6))OM012 <- table(round(Matriz_AS$OM012,0))
OM012_df <- rbind(as.data.frame(OM012))
OM012_plot <- ggplot(OM012_df,aes(x=Var1,y=Freq,fill=Freq))+geom_col()+geom_line(group=1)+theme_minimal()+theme(legend.position="none")+labs(x="OM012")+theme(axis.title.x = element_text(face="bold", vjust=-0.5, colour="orange", size=rel(1)),axis.title=element_text(size=10,face="bold"))+theme(axis.text.x = element_text(angle = 90, hjust = 1),axis.text = element_text(size=6))OM015 <- table(round(Matriz_AS$OM015,0))
OM015_df <- rbind(as.data.frame(OM015))
OM015_plot <- ggplot(OM015_df,aes(x=Var1,y=Freq,fill=Freq))+geom_col()+geom_line(group=1)+theme_minimal()+theme(legend.position="none")+labs(x="OM015")+theme(axis.title.x = element_text(face="bold", vjust=-0.5, colour="orange", size=rel(1)),axis.title=element_text(size=10,face="bold"))+theme(axis.text.x = element_text(angle = 90, hjust = 1),axis.text = element_text(size=6))OM019 <- table(round(Matriz_AS$OM019,0))
OM019_df <- rbind(as.data.frame(OM019))
OM019_plot <- ggplot(OM019_df,aes(x=Var1,y=Freq,fill=Freq))+geom_col()+geom_line(group=1)+theme_minimal()+theme(legend.position="none")+labs(x="OM019")+theme(axis.title.x = element_text(face="bold", vjust=-0.5, colour="orange", size=rel(1)),axis.title=element_text(size=10,face="bold"))+theme(axis.text.x = element_text(angle = 90, hjust = 1),axis.text = element_text(size=6))OM025 <- table(round(Matriz_AS$OM025,0))
OM025_df <- rbind(as.data.frame(OM025))
OM025_plot <- ggplot(OM025_df,aes(x=Var1,y=Freq,fill=Freq))+geom_col()+geom_line(group=1)+theme_minimal()+theme(legend.position="none")+labs(x="OM025")+theme(axis.title.x = element_text(face="bold", vjust=-0.5, colour="orange", size=rel(1)),axis.title=element_text(size=10,face="bold"))+theme(axis.text.x = element_text(angle = 90, hjust = 1),axis.text = element_text(size=6))OM028 <- table(round(Matriz_AS$OM028,0))
OM028_df <- rbind(as.data.frame(OM028))
OM028_plot <- ggplot(OM028_df,aes(x=Var1,y=Freq,fill=Freq))+geom_col()+geom_line(group=1)+theme_minimal()+theme(legend.position="none")+labs(x="OM028")+theme(axis.title.x = element_text(face="bold", vjust=-0.5, colour="orange", size=rel(1)),axis.title=element_text(size=10,face="bold"))+theme(axis.text.x = element_text(angle = 90, hjust = 1),axis.text = element_text(size=6))OM029 <- table(round(Matriz_AS$OM029,0))
OM029_df <- rbind(as.data.frame(OM029))
OM029_plot <- ggplot(OM029_df,aes(x=Var1,y=Freq,fill=Freq))+geom_col()+geom_line(group=1)+theme_minimal()+theme(legend.position="none")+labs(x="OM029")+theme(axis.title.x = element_text(face="bold", vjust=-0.5, colour="orange", size=rel(1)),axis.title=element_text(size=10,face="bold"))+theme(axis.text.x = element_text(angle = 90, hjust = 1),axis.text = element_text(size=6))OM032 <- table(round(Matriz_AS$OM032,0))
OM032_df <- rbind(as.data.frame(OM032))
OM032_plot <- ggplot(OM032_df,aes(x=Var1,y=Freq,fill=Freq))+geom_col()+geom_line(group=1)+theme_minimal()+theme(legend.position="none")+labs(x="OM032")+theme(axis.title.x = element_text(face="bold", vjust=-0.5, colour="orange", size=rel(1)),axis.title=element_text(size=10,face="bold"))+theme(axis.text.x = element_text(angle = 90, hjust = 1),axis.text = element_text(size=6))OM04 <- table(round(Matriz_AS$OM04,0))
OM04_df <- rbind(as.data.frame(OM04))
OM04_plot <- ggplot(OM04_df,aes(x=Var1,y=Freq,fill=Freq))+geom_col()+geom_line(group=1)+theme_minimal()+theme(legend.position="none")+labs(x="OM04")+theme(axis.title.x = element_text(face="bold", vjust=-0.5, colour="orange", size=rel(1)),axis.title=element_text(size=10,face="bold"))+theme(axis.text.x = element_text(angle = 90, hjust = 1),axis.text = element_text(size=6))OM05 <- table(round(Matriz_AS$OM05,0))
OM05_df <- rbind(as.data.frame(OM05))
OM05_plot <- ggplot(OM05_df,aes(x=Var1,y=Freq,fill=Freq))+geom_col()+geom_line(group=1)+theme_minimal()+theme(legend.position="none")+labs(x="OM05")+theme(axis.title.x = element_text(face="bold", vjust=-0.5, colour="orange", size=rel(1)),axis.title=element_text(size=10,face="bold"))+theme(axis.text.x = element_text(angle = 90, hjust = 1),axis.text = element_text(size=6))OM06 <- table(round(Matriz_AS$OM06,0))
OM06_df <- rbind(as.data.frame(OM06))
OM06_plot <- ggplot(OM06_df,aes(x=Var1,y=Freq,fill=Freq))+geom_col()+geom_line(group=1)+theme_minimal()+theme(legend.position="none")+labs(x="OM06")+theme(axis.title.x = element_text(face="bold", vjust=-0.5, colour="orange", size=rel(1)),axis.title=element_text(size=10,face="bold"))+theme(axis.text.x = element_text(angle = 90, hjust = 1),axis.text = element_text(size=6))OM009 <- table(round(Matriz_AS$OM009,0))
OM009_df <- rbind(as.data.frame(OM009))
OM009_plot <- ggplot(OM009_df,aes(x=Var1,y=Freq,fill=Freq))+geom_col()+geom_line(group=1)+theme_minimal()+theme(legend.position="none")+labs(x="OM009")+theme(axis.title.x = element_text(face="bold", vjust=-0.5, colour="orange", size=rel(1)),axis.title=element_text(size=10,face="bold"))+theme(axis.text.x = element_text(angle = 90, hjust = 1),axis.text = element_text(size=6))OM33 <- table(round(Matriz_AS$OM33,0))
OM33_df <- rbind(as.data.frame(OM33))
OM33_plot <- ggplot(OM33_df,aes(x=Var1,y=Freq,fill=Freq))+geom_col()+geom_line(group=1)+theme_minimal()+theme(legend.position="none")+labs(x="OM33")+theme(axis.title.x = element_text(face="bold", vjust=-0.5, colour="orange", size=rel(1)),axis.title=element_text(size=10,face="bold"))+theme(axis.text.x = element_text(angle = 90, hjust = 1),axis.text = element_text(size=6))OM49 <- table(round(Matriz_AS$OM49,0))
OM49_df <- rbind(as.data.frame(OM49))
OM49_plot <- ggplot(OM49_df,aes(x=Var1,y=Freq,fill=Freq))+geom_col()+geom_line(group=1)+theme_minimal()+theme(legend.position="none")+labs(x="OM49")+theme(axis.title.x = element_text(face="bold", vjust=-0.5, colour="orange", size=rel(1)),axis.title=element_text(size=10,face="bold"))+theme(axis.text.x = element_text(angle = 90, hjust = 1),axis.text = element_text(size=6))grid.arrange(OM001_plot,OM010_plot,OM012_plot,OM015_plot,OM019_plot,OM025_plot,OM028_plot,OM029_plot,OM032_plot,OM04_plot,OM05_plot,OM06_plot,OM009_plot,OM33_plot,OM49_plot,ncol=5)
AS_matriz <- exprMatrix_AS
AS_matriz$ids <- gsub('_.*$', '',AS_matriz$ids)
isoforma_y_gen_AS <- OUT_AS[,c(1,7)]
isoforma_y_gen_AS$associated_gene <- gsub('\\..*$', '',isoforma_y_gen_AS$associated_gene)
isoforma_id_nombre_AS <-dplyr::inner_join(isoforma_y_gen_AS,ID_AS,by=c("associated_gene" = "X1"))
matrix_nombre_id_AS <- dplyr::inner_join(isoforma_id_nombre_AS,AS_matriz,by=c("isoform" = "ids"))ExpresiĆ³n media
para_media <- matrix_nombre_id_AS
AS_TpmRNA_mean <- apply(para_media[,c(4:18)], 1, mean) #Media de expresiĆ³n para cada isoforma para los genes antisentido
out_cambiado_prot_coding <- cbind(para_media,AS_TpmRNA_mean)
isoforma_y_media_AS <- out_cambiado_prot_coding[,c(1,2,3,19)]
datatable(isoforma_y_media_AS)Siguiendo el modelo anterior se realizan los mismos para agrupar la expresiĆ³n de cada isoforma en base al nĆŗmero entero de su expresiĆ³n en TPM
Isoform <- table(round(isoforma_y_media_AS$AS_TpmRNA_mean,0))
isoform_df <- rbind(as.data.frame(Isoform))
isoform_plot <- ggplot(isoform_df,aes(x=Var1,y=Freq,fill=Freq))+geom_col()+theme_minimal()+theme(legend.position="none")+labs(x="ExpresiĆ³n TPM")+theme(axis.title.x = element_text(face="bold", vjust=-0.5, colour="orange", size=rel(1)),axis.title=element_text(size=10,face="bold"))+theme(axis.text.x = element_text(angle = 90, hjust = 1),axis.text = element_text(size=6))+geom_line(group=1)
isoform_plot
isoform_mean_AS <- isoforma_y_media_AS[,c(1,4)]
id_nombre <-dplyr::inner_join(uniq_matriz_AS_no_gene_id,isoform_mean_AS,by="isoform")
uniq_AS_no_expr_gen_name_id <- id_nombre %>% distinct(isoform, .keep_all = TRUE)genes_AS_no_S <- table(uniq_AS_no_expr_gen_name_id$X7)OUT_Prot_coding$associated_gene <- gsub('\\..*$', '',OUT_Prot_coding$associated_gene)
table_out_gene <- table(OUT_Prot_coding$associated_gene)
df_gene_total <- rbind(as.data.frame(table_out_gene))ID_gene <- prot_coding_comparar_1556[,c(5,6)]
t2<- ID_gene %>% distinct(X5, .keep_all = TRUE)id_nombre <-dplyr::inner_join(df_gene_total,t2,by=c("Var1" = "X5"))
id_nombre_gen <- id_nombre[,c(1,3)]media_expresion_mas_gen <-dplyr::inner_join(isoforma_y_media_AS,t_id_AS_gene,by=c("associated_gene" = "X1"))3.2 Genes S
Para los genes sentido sentido se sigue el mismo modelo anterior.
OUT_prueba <- exprMatrix_Prot_coding
colnames(OUT_prueba) <- c("ID","OM001","OM010","OM012","OM015","OM019","OM025","OM028","OM029","OM032","OM04","OM05","OM06","OM009","OM33","OM49")
heatmap_AS <- OUT_prueba
heatmap_matrix_AS <- as.matrix(heatmap_AS[,2:16])
rownames(heatmap_matrix_AS) <- exprMatrix_Prot_coding$ids
heatmap(heatmap_matrix_AS,Rowv=NA,Colv=NA,col= colorRampPalette(brewer.pal(5,"YlOrRd"))(4),cexRow=0.001)
OM001 <- table(round(OUT_prueba$OM001,0))
OM001_df <- rbind(as.data.frame(OM001))
OM001_plot <- ggplot(OM001_df,aes(x=Var1,y=Freq,fill=Freq))+geom_col()+geom_line(group=1)+theme_minimal()+theme(legend.position="none")+labs(x="OM001")+theme(axis.title.x = element_text(face="bold", vjust=-0.5, colour="orange", size=rel(1)),axis.title=element_text(size=10,face="bold"))+theme(axis.text.x = element_text(angle = 90, hjust = 1),axis.text = element_text(size=2))OM010 <- table(round(OUT_prueba$OM010,0))
OM010_df <- rbind(as.data.frame(OM010))
OM010_plot <- ggplot(OM010_df,aes(x=Var1,y=Freq,fill=Freq))+geom_col()+geom_line(group=1)+theme_minimal()+theme(legend.position="none")+labs(x="OM010")+theme(axis.title.x = element_text(face="bold", vjust=-0.5, colour="orange", size=rel(1)),axis.title=element_text(size=10,face="bold"))+theme(axis.text.x = element_text(angle = 90, hjust = 1),axis.text = element_text(size=2))OM012 <- table(round(OUT_prueba$OM012,0))
OM012_df <- rbind(as.data.frame(OM012))
OM012_plot <- ggplot(OM012_df,aes(x=Var1,y=Freq,fill=Freq))+geom_col()+geom_line(group=1)+theme_minimal()+theme(legend.position="none")+labs(x="OM012")+theme(axis.title.x = element_text(face="bold", vjust=-0.5, colour="orange", size=rel(1)),axis.title=element_text(size=10,face="bold"))+theme(axis.text.x = element_text(angle = 90, hjust = 1),axis.text = element_text(size=2))OM015 <- table(round(OUT_prueba$OM015,0))
OM015_df <- rbind(as.data.frame(OM015))
OM015_plot <- ggplot(OM015_df,aes(x=Var1,y=Freq,fill=Freq))+geom_col()+geom_line(group=1)+theme_minimal()+theme(legend.position="none")+labs(x="OM015")+theme(axis.title.x = element_text(face="bold", vjust=-0.5, colour="orange", size=rel(1)),axis.title=element_text(size=10,face="bold"))+theme(axis.text.x = element_text(angle = 90, hjust = 1),axis.text = element_text(size=2))OM019 <- table(round(OUT_prueba$OM019,0))
OM019_df <- rbind(as.data.frame(OM019))
OM019_plot <- ggplot(OM019_df,aes(x=Var1,y=Freq,fill=Freq))+geom_col()+geom_line(group=1)+theme_minimal()+theme(legend.position="none")+labs(x="OM019")+theme(axis.title.x = element_text(face="bold", vjust=-0.5, colour="orange", size=rel(1)),axis.title=element_text(size=10,face="bold"))+theme(axis.text.x = element_text(angle = 90, hjust = 1),axis.text = element_text(size=2))OM025 <- table(round(OUT_prueba$OM025,0))
OM025_df <- rbind(as.data.frame(OM025))
OM025_plot <- ggplot(OM025_df,aes(x=Var1,y=Freq,fill=Freq))+geom_col()+geom_line(group=1)+theme_minimal()+theme(legend.position="none")+labs(x="OM025")+theme(axis.title.x = element_text(face="bold", vjust=-0.5, colour="orange", size=rel(1)),axis.title=element_text(size=10,face="bold"))+theme(axis.text.x = element_text(angle = 90, hjust = 1),axis.text = element_text(size=2))OM028 <- table(round(OUT_prueba$OM028,0))
OM028_df <- rbind(as.data.frame(OM028))
OM028_plot <- ggplot(OM028_df,aes(x=Var1,y=Freq,fill=Freq))+geom_col()+geom_line(group=1)+theme_minimal()+theme(legend.position="none")+labs(x="OM028")+theme(axis.title.x = element_text(face="bold", vjust=-0.5, colour="orange", size=rel(1)),axis.title=element_text(size=10,face="bold"))+theme(axis.text.x = element_text(angle = 90, hjust = 1),axis.text = element_text(size=2))OM029 <- table(round(OUT_prueba$OM029,0))
OM029_df <- rbind(as.data.frame(OM029))
OM029_plot <- ggplot(OM029_df,aes(x=Var1,y=Freq,fill=Freq))+geom_col()+geom_line(group=1)+theme_minimal()+theme(legend.position="none")+labs(x="OM029")+theme(axis.title.x = element_text(face="bold", vjust=-0.5, colour="orange", size=rel(1)),axis.title=element_text(size=10,face="bold"))+theme(axis.text.x = element_text(angle = 90, hjust = 1),axis.text = element_text(size=2))OM032 <- table(round(OUT_prueba$OM032,0))
OM032_df <- rbind(as.data.frame(OM032))
OM032_plot <- ggplot(OM032_df,aes(x=Var1,y=Freq,fill=Freq))+geom_col()+geom_line(group=1)+theme_minimal()+theme(legend.position="none")+labs(x="OM032")+theme(axis.title.x = element_text(face="bold", vjust=-0.5, colour="orange", size=rel(1)),axis.title=element_text(size=10,face="bold"))+theme(axis.text.x = element_text(angle = 90, hjust = 1),axis.text = element_text(size=2))OM04 <- table(round(OUT_prueba$OM04,0))
OM04_df <- rbind(as.data.frame(OM04))
OM04_plot <- ggplot(OM04_df,aes(x=Var1,y=Freq,fill=Freq))+geom_col()+geom_line(group=1)+theme_minimal()+theme(legend.position="none")+labs(x="OM04")+theme(axis.title.x = element_text(face="bold", vjust=-0.5, colour="orange", size=rel(1)),axis.title=element_text(size=10,face="bold"))+theme(axis.text.x = element_text(angle = 90, hjust = 1),axis.text = element_text(size=2))OM05 <- table(round(OUT_prueba$OM05,0))
OM05_df <- rbind(as.data.frame(OM05))
OM05_plot <- ggplot(OM05_df,aes(x=Var1,y=Freq,fill=Freq))+geom_col()+geom_line(group=1)+theme_minimal()+theme(legend.position="none")+labs(x="OM05")+theme(axis.title.x = element_text(face="bold", vjust=-0.5, colour="orange", size=rel(1)),axis.title=element_text(size=10,face="bold"))+theme(axis.text.x = element_text(angle = 90, hjust = 1),axis.text = element_text(size=2))OM06 <- table(round(OUT_prueba$OM06,0))
OM06_df <- rbind(as.data.frame(OM06))
OM06_plot <- ggplot(OM06_df,aes(x=Var1,y=Freq,fill=Freq))+geom_col()+geom_line(group=1)+theme_minimal()+theme(legend.position="none")+labs(x="OM06")+theme(axis.title.x = element_text(face="bold", vjust=-0.5, colour="orange", size=rel(1)),axis.title=element_text(size=10,face="bold"))+theme(axis.text.x = element_text(angle = 90, hjust = 1),axis.text = element_text(size=2))OM009 <- table(round(OUT_prueba$OM009,0))
OM009_df <- rbind(as.data.frame(OM009))
OM009_plot <- ggplot(OM009_df,aes(x=Var1,y=Freq,fill=Freq))+geom_col()+geom_line(group=1)+theme_minimal()+theme(legend.position="none")+labs(x="OM009")+theme(axis.title.x = element_text(face="bold", vjust=-0.5, colour="orange", size=rel(1)),axis.title=element_text(size=10,face="bold"))+theme(axis.text.x = element_text(angle = 90, hjust = 1),axis.text = element_text(size=2))OM33 <- table(round(OUT_prueba$OM33,0))
OM33_df <- rbind(as.data.frame(OM33))
OM33_plot <- ggplot(OM33_df,aes(x=Var1,y=Freq,fill=Freq))+geom_col()+geom_line(group=1)+theme_minimal()+theme(legend.position="none")+labs(x="OM33")+theme(axis.title.x = element_text(face="bold", vjust=-0.5, colour="orange", size=rel(1)),axis.title=element_text(size=10,face="bold"))+theme(axis.text.x = element_text(angle = 90, hjust = 1),axis.text = element_text(size=2))OM49 <- table(round(OUT_prueba$OM49,0))
OM49_df <- rbind(as.data.frame(OM49))
OM49_plot <- ggplot(OM49_df,aes(x=Var1,y=Freq,fill=Freq))+geom_col()+geom_line(group=1)+theme_minimal()+theme(legend.position="none")+labs(x="OM49")+theme(axis.title.x = element_text(face="bold", vjust=-0.5, colour="orange", size=rel(1)),axis.title=element_text(size=10,face="bold"))+theme(axis.text.x = element_text(angle = 90, hjust = 1),axis.text = element_text(size=6))grid.arrange(OM001_plot,OM010_plot,OM012_plot,OM015_plot,OM019_plot,OM025_plot,OM028_plot,OM029_plot,OM032_plot,OM04_plot,OM05_plot,OM06_plot,OM009_plot,OM33_plot,OM49_plot,ncol=5)
gene_matriz <- exprMatrix_Prot_coding
gene_matriz$ids <- gsub('_.*$', '',gene_matriz$ids)
isoforma_y_gen <- OUT_Prot_coding[,c(1,7)]
isoforma_y_gen$associated_gene <- gsub('\\..*$', '',isoforma_y_gen$associated_gene)
isoforma_id_gen <-dplyr::inner_join(isoforma_y_gen,id_nombre_gen ,by=c("associated_gene" = "Var1"))
matrix_nombre_id <- dplyr::inner_join(isoforma_id_gen,gene_matriz,by=c("isoform" = "ids"))para_media <- matrix_nombre_id
AS_TpmRNA_mean <- apply(para_media[,c(4:18)], 1, mean)
out_cambiado_prot_coding <- cbind(para_media,AS_TpmRNA_mean)
isoforma_y_media_gen <- out_cambiado_prot_coding[,c(1,2,3,19)]t7 <- isoforma_y_media_gen %>% distinct(isoform, .keep_all = TRUE)
datatable(t7)Isoform <- table(round(t7$AS_TpmRNA_mean,0))
isoform_df <- rbind(as.data.frame(Isoform))
isoform_plot <- ggplot(isoform_df,aes(x=Var1,y=Freq,fill=Freq))+geom_col()+theme_minimal()+theme(legend.position="none")+labs(x="ExpresiĆ³n TPM")+theme(axis.title.x = element_text(face="bold", vjust=-0.5, colour="orange", size=rel(1)),axis.title=element_text(size=10,face="bold"))+theme(axis.text.x = element_text(angle = 90, hjust = 1),axis.text = element_text(size=6))+geom_line(group=1)
isoform_plot
AS_gen <- media_expresion_mas_gen[,c(5,3)]df_genes <- rbind(as.data.frame(table(t7$X6)))
gen_id <- t7[,c(2,3)]
t18 <- gen_id %>% distinct(associated_gene, .keep_all = TRUE)
gen_sin_AS <- dplyr::left_join(t18,AS_gen,by=c("X6"="X8"))
t28 <- gen_sin_AS %>% distinct(associated_gene, .keep_all = TRUE)
datatable(t28)4 Genes S y AS comparados
media_expresion_mas_gen_AS <-dplyr::inner_join(media_expresion_mas_gen,isoforma_y_media_gen,by=c("X8" = "X6"))
datatable(media_expresion_mas_gen_AS)4.1 Genes sin AS.
Genes_sin_expresion_AS <- read_excel("~/Desktop/tablas_expresion/Genes_sin_expresion_AS.xlsx",col_names = FALSE)genes_sin_AS <-dplyr::inner_join(t7,Genes_sin_expresion_AS,by=c("associated_gene" = "...1"))Genes_sin_AS_S <- genes_sin_AS[,c(1,2,3,4)]
t100 <- Genes_sin_AS_S %>% distinct(isoform, .keep_all = TRUE)
datatable(t100)##Ā Genes S y AS.
solo_AS_con_S <- media_expresion_mas_gen_AS %>% distinct(isoform.x, .keep_all = TRUE) #SelecciĆ³n para isoformas Ćŗnicas de genes AS
solo_S_con_AS <- media_expresion_mas_gen_AS %>% distinct(isoform.y, .keep_all = TRUE) #SelecciĆ³n para isoformas Ćŗnicas de genes S
#Esto se realiza para poder seleccionar las columnas Ćŗnicas para cada tipo de dato ya que al utilizar left o inner join se pueden generar datos duplicados para cumplir todas las correspondencias. 4.1.1 ExpresiĆ³n
TPM_mean_GEN_sin_AS <- mean(t100$AS_TpmRNA_mean)
TPM_solo_AS_con_S <- mean(solo_AS_con_S$AS_TpmRNA_mean.x)
TPM_solo_S_con_AS <- mean(solo_S_con_AS$AS_TpmRNA_mean.y)medias_data_frame <- data.frame("Gen Sin AS"=TPM_mean_GEN_sin_AS, "AS con S"=TPM_solo_AS_con_S, "S con AS"=TPM_solo_S_con_AS) #Dara frame para relacionar la media de expresiĆ³n de cada tipo de dato#ComparaciĆ³n de la mediana de expresiĆ³n para cada valor obtenido anteriormente
TPM_median_GEN_sin_AS <- median(t100$AS_TpmRNA_mean)
TPM_median_solo_AS_con_S <- median(solo_AS_con_S$AS_TpmRNA_mean.x)
TPM_median_solo_S_con_AS <- median(solo_S_con_AS$AS_TpmRNA_mean.y)
median_data_frame <- data.frame("Type"=c("Gen_sin_AS","AS_con_S","S_con_AS"),"Mean"=c(TPM_median_GEN_sin_AS,TPM_median_solo_AS_con_S,TPM_median_solo_S_con_AS))
barplot(c(TPM_median_solo_AS_con_S,TPM_median_solo_S_con_AS),names.arg = c("AS","S"), main = "Mediana de TPM de expresiĆ³n", xlab="TPM", col=c("lightcoral","lightblue"),horiz = TRUE,las=1, cex.names = 0.9,cex.axis=0.8,xlim=c(0,7.5))
wilcox.test(solo_AS_con_S$AS_TpmRNA_mean.x,solo_S_con_AS$AS_TpmRNA_mean.y)##
## Wilcoxon rank sum test with continuity correction
##
## data: solo_AS_con_S$AS_TpmRNA_mean.x and solo_S_con_AS$AS_TpmRNA_mean.y
## W = 7260.5, p-value = 5.32e-09
## alternative hypothesis: true location shift is not equal to 0isoform_median_solo_AS_con_S <- table(solo_AS_con_S$X7)
isoform_median_solo_S_con_AS <- table(solo_S_con_AS$X8)
barplot(c(median(isoform_median_solo_AS_con_S),median(isoform_median_solo_S_con_AS)),names.arg = c("AS","S"), main = "Mediana de transcritos", xlab="NĆŗmero de transcritos", col=c("lightcoral","lightblue"),horiz = TRUE,las=1, cex.names = 0.9,cex.axis=0.8,xlim=c(0,1.1))
TPM_median_GEN_sin_AS <- median(t100$AS_TpmRNA_mean)
TPM_median_solo_AS_con_S <- median(solo_AS_con_S$AS_TpmRNA_mean.x)
barplot(c(TPM_median_GEN_sin_AS,TPM_median_solo_S_con_AS),names.arg = c("No","Si"), main = "Mediana de TPM de expresiĆ³n", xlab="TPM", col=c("lightcoral","lightblue"),horiz = TRUE,las=1, cex.names = 0.9,cex.axis=0.8,xlim=c(0,7.5))
wilcox.test(t100$AS_TpmRNA_mean,solo_S_con_AS$AS_TpmRNA_mean.y)##
## Wilcoxon rank sum test with continuity correction
##
## data: t100$AS_TpmRNA_mean and solo_S_con_AS$AS_TpmRNA_mean.y
## W = 96125, p-value = 0.4894
## alternative hypothesis: true location shift is not equal to 0TPM_median_GEN_sin_AS <- median(t100$AS_TpmRNA_mean)
TPM_median_solo_AS_con_S <- median(solo_AS_con_S$AS_TpmRNA_mean.x)
TPM_median_solo_S_con_AS <- median(solo_S_con_AS$AS_TpmRNA_mean.y)
isoform_gen_sin_As <- table(t100$X6)
isoform_gen_con_AS <- table(solo_AS_con_S$X8)
barplot(c(median(isoform_gen_sin_As),median(isoform_gen_con_AS)),names.arg = c("No","Si"), main = "Mediana de transcritos", xlab="NĆŗmero de transcritos", col=c("lightcoral","lightblue"),horiz = TRUE,las=1, cex.names = 0.9,cex.axis=0.8,xlim=c(0,1.1))
4.2 AS sin S
AS_matriz <- exprMatrix_AS
AS_matriz$ids <- gsub('_.*$', '',AS_matriz$ids)
isoforma_id_nombre <-dplyr::inner_join(isoforma_y_gen_AS,ID_AS,by=c("associated_gene" = "X1"))
id_gen_noAS_noexprx <- id_AS_name_no_expr_sent[,c(1,3)]
isoforma_id_nombre <-dplyr::inner_join(isoforma_id_nombre,id_gen_noAS_noexprx,by=c("associated_gene" = "X1"))
isoform_TPM_medio <- isoforma_y_media_AS[,c(1,4)]
uniq_matriz_AS_no_gene_id <- isoforma_id_nombre %>% distinct(isoform, .keep_all = TRUE)tabla_AS_no_S_tpm_medio <- dplyr::inner_join(uniq_matriz_AS_no_gene_id,isoform_TPM_medio,by="isoform")
datatable(tabla_AS_no_S_tpm_medio)sin_gen_S <- tabla_AS_no_S_tpm_medio[,c(1,2,3,5)]
AS_S_no_S <- tabla_AS_no_S_tpm_medio[,c(3,4)]barplot(c(median(sin_gen_S$AS_TpmRNA_mean),median(solo_AS_con_S$AS_TpmRNA_mean.x)),names.arg = c("No","Si"), main = "Mediana de TPM de expresiĆ³n", xlab="TPM", col=c("lightcoral","lightblue"),horiz = TRUE,las=1, cex.names = 0.9,cex.axis=0.8,xlim=c(0,3.5))
wilcox.test(sin_gen_S$AS_TpmRNA_mean,solo_AS_con_S$AS_TpmRNA_mean.x)##
## Wilcoxon rank sum test with continuity correction
##
## data: sin_gen_S$AS_TpmRNA_mean and solo_AS_con_S$AS_TpmRNA_mean.x
## W = 3696.5, p-value = 0.4903
## alternative hypothesis: true location shift is not equal to 0transcritos_AS_no_S <- table(sin_gen_S$X7)
transcritos_AS_si_AS <- table(solo_AS_con_S$X7)
barplot(c(median(transcritos_AS_no_S),median(transcritos_AS_si_AS)),names.arg = c("No","Si"), main = "Mediana de transcritos", xlab="NĆŗmero de transcritos", col=c("lightcoral","lightblue"),horiz = TRUE,las=1, cex.names = 0.9,cex.axis=0.8,xlim=c(0,1.1))
wilcox.test(transcritos_AS_no_S,transcritos_AS_si_AS)##
## Wilcoxon rank sum test with continuity correction
##
## data: transcritos_AS_no_S and transcritos_AS_si_AS
## W = 2409, p-value = 0.2465
## alternative hypothesis: true location shift is not equal to 0